Identification of recurrent chromosomal aberrations is important for diagnosis, prognosis, and therapy of most hematological malignancies. Due to difficulties with culture of tumor cells, low mitotic index, poor chromosomal morphologies, and low prevalence, it takes tremendous effort and time for a cytogenetic clinician to obtain a sufficient number of analyzable metaphase cells under microscope before he/she can make an accurate clinical diagnosis. This process is not only very inefficient but also subject to human errors. In order to improve the efficiency and accuracy of leukemia diagnosis, we propose to develop a computer aided chromosome imaging technique. Specifically, we will develop an innovative high-speed microscopic imaging system based on a time-delay-integration technique. The system can scan the entire sample-slide at high magnification to obtain high resolution digital images to reveal metaphase chromosomes as required by clinical diagnosis. We will also develop a novel computer aided diagnosis (CAD) scheme including four specific modules to (1) detect analyzable metaphase chromosome cells, (2) segment overlapped chromosomes, (3) identify and classify distorted chromosomes associated with cancer cells, and (4) predict the cancer prognosis. After identification and segmentation of analyzable chromosomes, we will compute and search for the effective and robust image features. Genetic algorithm will be used to train and optimize an artificial neural network and a Bayesian belief network for the classification and prediction tasks, respectively. Using the integrated CAD workstation, we will conduct an observer performance study to assess the performance of the technique and its clinical feasibility. In summary, the proposed imaging technique is highly efficient, and no or only minimal human interventions are required from initial slide-scanning up to the presentation of CAD results. With such a new computerized clinical tool, cytogeneticists can effectively focus their efforts on analyzing/verifying chromosomal abnormal patterns and making final diagnostic decisions. It is therefore expected that the proposed technology can significantly improve the efficiency and accuracy of cancer (i.e., leukemia) diagnosis. The proposed technique has significant clinical potentials in monitoring therapeutic efficacy of cancer treatment as well.